Authors: F. Di Martino, V. Loia, S. Sessa
Addresses: Universita degli Studi di Salerno, Dipartimento di Matematica e Informatica, Via Ponte Don Melillo, 84081 Fisciano (Salerno), Italy. ' Universita degli Studi di Salerno, Dipartimento di Matematica e Informatica, Via Ponte Don Melillo, 84081 Fisciano (Salerno), Italy. ' Universita degli Studi di Napoli Federico II, Dipartimento di Costruzioni e Metodi Matematici in Architettura, Via Monteoliveto 3, 80134 Napoli, Italy
Abstract: We use a hybrid approach based on a genetic algorithm and on the gradient descent method for image decomposition problem. We adopt an iterative gradient descent method, already used in a previous paper and here improved, in order to reconstruct an image by using an optimisation task based on the minimisation of a cost function. By normalising the values of its pixels with respect to the grey scale used, an image R is interpreted as a fuzzy relation. In order to obtain better results in terms of quality of the reconstructed image, we use a preprocessing genetic algorithm for determining two initial families of fuzzy sets that compose R in accordance to the concept of Schein rank of R. The experiments are executed on some images extracted from the SIDBA standard image database.
Keywords: Schein rank; image decomposition; fuzzy relations; genetic algorithms; GAs; descent gradient algorithm; fuzzy sets.
International Journal of Reasoning-based Intelligent Systems, 2009 Vol.1 No.1/2, pp.77 - 84
Published online: 24 Jun 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article